Automatic speech recognition has recently seen a significant advancement with large foundational models such as Whisper. However, these models often struggle to perform well in low-resource languages, such as Indian languages. This paper explores two novel approaches to enhance Whisper's multilingual speech recognition performance in Indian languages. First, we propose prompt-tuning with language family information, which enhances Whisper's accuracy in linguistically similar languages. Second, we introduce a novel tokenizer that reduces the number of generated tokens, thereby accelerating Whisper's inference speed. Our extensive experiments demonstrate that the tokenizer significantly reduces inference time, while prompt-tuning enhances accuracy across various Whisper model sizes, including Small, Medium, and Large. Together, these techniques achieve a balance between optimal WER and inference speed.

Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inver(cid:173) sion for ill-posed problems. Against this very general backdrop, any methods for improving the generalization performance, or for improving the speed in test phase, of SVMs are of increasing in(cid:173) terest. In this paper we combine two such techniques on a pattern recognition problem. The method for improving generalization per(cid:173) formance (the "virtual support vector" method) does so by incor(cid:173) porating known invariances of the problem. This method achieves a drop in the error rate on 10,000 NIST test digit images of 1.4% to 1.0%.

Users want natural language processing (NLP) systems to be both fast and accurate, but quality often comes at the cost of speed. The field has been manually exploring various speed-accuracy tradeoffs (for particular problems and datasets). We aim to explore this space automatically, focusing here on the case of agenda-based syntactic parsing \cite{kay-1986}. Unfortunately, off-the-shelf reinforcement learning techniques fail to learn good policies: the state space is simply too large to explore naively. An attempt to counteract this by applying imitation learning algorithms also fails: the teacher'' is far too good to successfully imitate with our inexpensive features.

Following best practices when building machine learning models is a time-consuming yet important process. There are so many things to do ranging from: preparing the data, selecting and training algorithms, understanding how the algorithm is making decisions, all the way down to deploying models to production. I like to think of the machine learning design and maintenance process as being comprised of ten steps (see the diagram above). But, if I want to save time, increase accuracy, and reduce risk, I don't manually go through the entire machine learning process in order to build my machine learning models. Instead, I turn to automated machine learning, using clever software that knows how to automate the repetitive and mundane steps, and freeing me up to do what humans are best at: communication, applying common sense, and being creative.

Damaging earthquakes can strike at any time. While we can't prevent them from occurring, we can make sure casualties, economic loss and disruption of essential services are kept to a minimum. Building more resilient cities is key to withstanding earthquake disasters. If we had a better idea of when earthquakes would strike, authorities could initiate local emergency, evacuation and shelter plans. But unfortunately, this is not the case.

Artificial Intelligence / Machine Learning models, trained on visual representations of website code, can improve the accuracy and speed of detecting phishing websites. Artificial Intelligence (AI) and Machine Learning (ML) models that are trained on visual markups of a website code can enhance the accuracy and speed of detecting phishing websites. According to a document (PDF) published by security researchers at the University of Plymouth and the University of Portsmouth in the United Kingdom, this is the case. The researchers want to overcome the flaws in current detection technologies, which are either too sluggish or insufficiently accurate. Creating images from web code The researchers' method transforms the markup and code of web pages into images using "binary visualisation" tools.

Can we expect complete automation of medical processes in the near future, given the issues that AI systems face even in the most advanced areas of healthcare? I have touched on some of the aspects in my previous article, so now let's talk about more challenges facing the development and implementation of AI in the healthcare industry. The development of intelligent technologies is directly related to the method of creating AI -- or, more precisely, to the peculiarities of its training and processing the received data. The most typical tools, in this case, are neural networks and machine learning algorithms loaded with data from clinical databases and supported with the information about types of diagnostics and care provided. This mandates a good level of interaction between AI and actual databases and simultaneously triggers questions about the amount, versatility and representativeness of the cases included.

While visual'no code' tools are helping businesses get more out of computing without the need for armies of in-house techies to configure software on behalf of other staff, access to the most powerful tech tools -- at the'deep tech' AI coal face -- still requires some expert help (and/or costly in-house expertise). This is where bootstrapping French startup, NLPCloud.io, is plying a trade in MLOps/AIOps -- or'compute platform as a service' (being as it runs the queries on its own servers) -- with a focus on natural language processing (NLP), as its name suggests. Developments in artificial intelligence have, in recent years, led to impressive advances in the field of NLP -- a technology that can help businesses scale their capacity to intelligently grapple with all sorts of communications by automating tasks like Named Entity Recognition, sentiment-analysis, text classification, summarization, question answering, and Part-Of-Speech tagging, freeing up (human) staff to focus on more complex/nuanced work. OpenAI built a text generator so good, it's considered too dangerous to release Production ready (pre-trained) NLP models for English are readily available'out of the box'. There are also dedicated open source frameworks offering help with training models.

IMAGE: Generated heatmaps appropriately highlighted abnormalities in the lung fields in those images accurately labeled as COVID-19 positive (A-C) in contrast to images which were accurately labeled as negative for COVID-19... view more Called DeepCOVID-XR, the machine-learning algorithm outperformed a team of specialized thoracic radiologists -- spotting COVID-19 in X-rays about 10 times faster and 1-6% more accurately. The researchers believe physicians could use the A.I. system to rapidly screen patients who are admitted into hospitals for reasons other than COVID-19. Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner. The study's authors also believe the algorithm could potentially flag patients for isolation and testing who are not otherwise under investigation for COVID-19. The study will be published on Nov. 24 in the journal Radiology.

Neural networks are a fascinating field of computer science that attempts to model the brain in a mathematical sense. Unfortunately, they are nowhere near the level of complexity as the human brain, and will most likely not be in the near future. Neural networks are a fascinating field of computer science that attempts to model the brain in a mathematical sense. Unfortunately, they are nowhere near the level of complexity as the human brain, and will most likely not be in the near future. However, while there is no single network that can match the brain in its complexity and accuracy, there are specific networks that can rival the accuracy and speed of the human mind, though they often lag in the accuracy department.